Parallel evolutionary algorithms, studied to some extent over the past few years, have proven empirically worthwhile-though there seems to be lacking a better understanding of their workings. In this paper we concentrate on cellular (fine-grained) models, presenting a number of statistical measures, both at the genotypic and phenotypic levels. We demonstrate the application and utility of these measures on a specific example, that of the cellular programming evolutionary algorithm, when used to evolve solutions to a hard problem in the cellular-automata domain, known as synchronization.
CITATION STYLE
Capcarrère, M., Tettamanzi, A., Tomassini, M., & Sipper, M. (1998). Studying parallel evolutionary algorithms: The cellular programming case. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1498 LNCS, pp. 573–582). Springer Verlag. https://doi.org/10.1007/bfb0056899
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